Marinus J C Eijkemans1,2, Henri Leridon3, Niels Keiding4, Rémy Slama5. 1. From the Julius Center for Health Sciences and Primary Care, Department of Biostatistics and Research Support, University Medical Center, Utrecht, The Netherlands. 2. Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands. 3. INED (French Institute for Demographic Studies) and French Academy of Sciences, Paris, France. 4. Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark. 5. Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, U1209, Inserm, CNRS and University Grenoble-Alpes Joint Research Center (IAB), Grenoble, France.
Abstract
BACKGROUND: Several epidemiologic designs allow studying fecundability, the monthly probability of pregnancy occurrence in noncontracepting couples in the general population. These designs may, to varying extents, suffer from attenuation bias and other biases. We aimed to compare the main designs: incident and prevalent cohorts, pregnancy-based, and current duration approaches. METHODS: A realistic simulation model produced individual reproductive lives of a fictitious population. We drew random population samples according to each study design, from which the cumulative probability of pregnancy was estimated. We compared the abilities of the designs to highlight the impact of an environmental factor influencing fecundability, relying on the Cox model with censoring after 12 or 6 months. RESULTS: Regarding the estimation of the cumulative probability of pregnancy, the pregnancy-based approach was the most prone to bias. When we considered a hypothetical factor associated with a hazard ratio (HR) of pregnancy of 0.7, the estimated HR was in the 0.78-0.85 range, according to designs. This attenuation bias was largest for the prevalent cohort and smallest for the current duration approach, which had the largest variance. The bias could be limited in all designs by censoring durations at 6 months. CONCLUSION: Attenuation bias in HRs cannot be ignored in fecundability studies. Focusing on the effect of exposures during the first 6 months of unprotected intercourse through censoring removes part of this bias. For risk factors that can accurately be assessed retrospectively, retrospective fecundity designs, although biased, are not much more strongly so than logistically more intensive designs entailing follow-up.
BACKGROUND: Several epidemiologic designs allow studying fecundability, the monthly probability of pregnancy occurrence in noncontracepting couples in the general population. These designs may, to varying extents, suffer from attenuation bias and other biases. We aimed to compare the main designs: incident and prevalent cohorts, pregnancy-based, and current duration approaches. METHODS: A realistic simulation model produced individual reproductive lives of a fictitious population. We drew random population samples according to each study design, from which the cumulative probability of pregnancy was estimated. We compared the abilities of the designs to highlight the impact of an environmental factor influencing fecundability, relying on the Cox model with censoring after 12 or 6 months. RESULTS: Regarding the estimation of the cumulative probability of pregnancy, the pregnancy-based approach was the most prone to bias. When we considered a hypothetical factor associated with a hazard ratio (HR) of pregnancy of 0.7, the estimated HR was in the 0.78-0.85 range, according to designs. This attenuation bias was largest for the prevalent cohort and smallest for the current duration approach, which had the largest variance. The bias could be limited in all designs by censoring durations at 6 months. CONCLUSION:Attenuation bias in HRs cannot be ignored in fecundability studies. Focusing on the effect of exposures during the first 6 months of unprotected intercourse through censoring removes part of this bias. For risk factors that can accurately be assessed retrospectively, retrospective fecundity designs, although biased, are not much more strongly so than logistically more intensive designs entailing follow-up.
Authors: Joseph B Stanford; Sydney K Willis; Elizabeth E Hatch; Kenneth J Rothman; Lauren A Wise Journal: Hum Reprod Date: 2020-10-01 Impact factor: 6.918
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Authors: Lauren A Wise; Amelia K Wesselink; Elizabeth E Hatch; Jennifer Weuve; Eleanor J Murray; Tanran R Wang; Ellen M Mikkelsen; Henrik Toft Sørensen; Kenneth J Rothman Journal: Epidemiology Date: 2020-09 Impact factor: 4.860